We are hiring an AI Engineer to design and operate production-grade AI/ML systems that directly improve product and operational outcomes.
Our current focus is building a robust AI Agent Platform that enables automation of complex workflows with strong control, validation and continuous learning.
This role requires a strong engineer with a general production system mindset who can build reliable systems in production. You will play a key role in building our agent platform, which requires strong backend coding skills. The ability to build frontend components and work as a full-stack engineer is highly preferred and considered a strong advantage.
Depending on priorities, you may also work on:
Search and retrieval systems (keyword, semantic, hybrid)
Recommendation, ranking and personalization systems
AI evaluation, monitoring and reliability systems
Other AI-powered product or operational systems with measurable business impact
What You Will Build & Responsibilities
Design and build end-to-end AI/ML systems from problem definition to production rollout and iteration.
Architect and build the core components of the AI Agent Platform to support complex, multi-step workflows and autonomous task execution.
Develop backend services and APIs that expose AI capabilities safely and reliably.
Apply AI processing techniques, including data processing and data labeling, to prepare high-quality datasets for model training.
Train and deploy machine learning models, ranging from recommendation models to fine-tuning LLMs for specific business and operational purposes.
Develop and evolve AI Agent workflows, including context handling, validation and fallback logic.
Build retrieval, search, ranking and personalization components that connect user intent to the right data or action.
Integrate LLMs with structured and unstructured data via context construction and tool-enabled workflows.
Build monitoring, evaluation and human-in-the-loop workflows for continuous improvement.
Requirements
Must-have:
Strong
software engineering foundation with a general production mindset and strong Python skills.
Deep understanding of AI and ML models, with hands-on experience in AI data processing and data labeling.
Proven experience in training machine learning models (e.g., recommendation models) and fine-tuning LLMs for practical applications.
Experience in AI Engineering or ML Engineering.
Experience building backend services and APIs.
Hands-on experience building LLM-based applications.
Understanding of RAG, embeddings, vector databases, retrieval and search pipelines.
Experience in at least one of the following: AI agent or multi-step AI workflows; Search, retrieval or relevance systems; Recommendation, ranking or personalization systems.
Nice to Have:
Full-stack development capabilities or experience building frontend interfaces for AI products.
Experience with large-scale search or recommendation systems.
Experience with agent orchestration frameworks or tool-using LLM systems.
Experience with A/B testing, experimentation or evaluation pipelines.
Experience with observability, monitoring and production reliability.
Experience with multimodal systems (voice, OCR, image).
Experience in high-scale operational domains (fintech, payments, customer operations).
Experience mentoring or leading projects.
How You Work
You are an engineer first: you care about clean code, maintainability and production reliability.
You have an AI mindset: you understand when to use rules, ML, LLMs or hybrid approaches.
You have an applied mindset: you start from the problem and choose the simplest effective solution.
You are proactive: you propose, build, test and improve continuously.
Success Metrics
Impact is measured by real outcomes in operations and product performance:
Reduce manual workload through automation of repetitive workflows.
Increase automation rate for eligible tasks.
Reduce system errors and escalation to human handling.
Improve output quality and overall user satisfaction.
Increase key product metrics such as CTR, conversion and payment rate.
Improve user retention and engagement.
Ensure system stability and scalability as traffic and complexity grow.
Maintain strong observability, control and continuous improvement via monitoring and feedback loops.
What Success Looks Like
Systems you build are deployed and used in production at scale.
Clear and measurable impact on business and product metrics.
High system reliability with strong quality control and guardrails.
Continuous improvement driven by evaluation and feedback.
Why Join us
Work on end-to-end AI systems, not isolated models.
Solve real-world problems at scale.
High ownership with visible impact on product and business metrics.
Opportunity to shape the architecture of AI platforms (Agents, Search, Recommendation).